Artificial intelligence systems and methods for generating educational inquiry responses from biological extractions

ABSTRACT

An artificial intelligence system for generating educational inquiry responses from biological extractions, the system comprising a computing device, the computing device designed and configured to retrieve a biological extraction pertaining to a user, receive, from a third-party device, an educational inquiry, select, based on the educational inquiry, at least a machine-learning process, and generate, using the at least a machine-learning process and the biological extraction, an inquiry response.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed toartificial intelligence systems and methods for generating educationalinquiry responses from biological extractions.

BACKGROUND

Efficient routing of inquiries to responses regarding educationalrequests remains elusive, owing to the divergent criteria according towhich suitability of such routing may be adjudged. A resulting lack ofspecificity may end in dissatisfaction with resulting outputs.

SUMMARY OF THE DISCLOSURE

In an aspect, an artificial intelligence system for generatingeducational inquiry responses from biological extractions, the systemcomprising a computing device, the computing device designed andconfigured to retrieve a biological extraction pertaining to a user,receive, from a third-party device, an educational inquiry, select,based on the educational inquiry, at least a machine-learning process,and generate, using the at least a machine-learning process and thebiological extraction, an inquiry response.

In another aspect, an artificial intelligence method of generatingeducational inquiry responses from biological extractions includesretrieving, by a computing device, a biological extraction pertaining toa user. The method includes receiving, by the computing device and froma third-party device, an educational inquiry. The method includesselecting, by the computing device and based on the educational inquiry,at least a machine-learning process. The method includes generating, bythe computing device and using the at least a machine-learning processand the biological extraction, an inquiry response.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of anartificial intelligence system for generating educational inquiryresponses from biological extractions;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 3 is a flow diagram illustrating an exemplary embodiment of anartificial intelligence method of generating educational inquiryresponses from biological extractions; and

FIG. 4 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments disclosed herein use machine-learning processes to select aresponse to an inquiry regarding education, based upon a biologicalextraction. Selection of machine-learning processes may be performed asa function of educational inquiries; selection may be performed viaclassification.

Referring now to FIG. 1, an exemplary embodiment of an artificialintelligence system 100 for generating educational inquiry responsesfrom biological extractions 108 is illustrated. System includes acomputing device 104. Computing device 104 may include any computingdevice 104 as described in this disclosure, including without limitationa microcontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing device104 may include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device 104 operating independently or mayinclude two or more computing device 104 operating in concert, inparallel, sequentially or the like; two or more computing devices 104may be included together in a single computing device 104 or in two ormore computing devices 104. Computing device 104 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting computing device 104 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computing devices104, and any combinations thereof. A network may employ a wired and/or awireless mode of communication. In general, any network topology may beused. Information (e.g., data, software etc.) may be communicated toand/or from a computer and/or a computing device 104. Computing device104 may include but is not limited to, for example, a computing device104 or cluster of computing devices 104 in a first location and a secondcomputing device 104 or cluster of computing devices 104 in a secondlocation. Computing device 104 may include one or more computing devices104 dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device 104 may distribute one ormore computing tasks as described below across a plurality of computingdevices 104 of computing device 104, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices 104. Computing device 104 maybe implemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device 104.

Continuing to refer to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Still referring to FIG. 1, computing device 104 may be configured toreceive a biological extraction 108 pertaining to a user. A “biologicalextraction 108” as used in this disclosure is an element of dataincluding at least an element of user physiological data. As used inthis disclosure, “physiological data” is any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. For instance, and withoutlimitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss.

With continued reference to FIG. 1, physiological state data mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline phosphatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibrinogen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1, physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices 104; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processing moduleas described in this disclosure. As a non-limiting example, biologicalextraction 108 may include a psychological profile; the psychologicalprofile may be obtained utilizing a questionnaire performed by the user.

Still referring to FIG. 1, physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences or other genetic sequencescontained in one or more chromosomes in human cells. Genomic data mayinclude, without limitation, ribonucleic acid (RNA) samples and/orsequences, such as samples and/or sequences of messenger RNA (mRNA) orthe like taken from human cells. Genetic data may include telomerelengths. Genomic data may include epigenetic data including datadescribing one or more states of methylation of genetic material.Physiological state data may include proteomic data, which as usedherein is data describing all proteins produced and/or modified by anorganism, colony of organisms, or system of organisms, and/or a subsetthereof. Physiological state data may include data concerning amicrobiome of a person, which as used herein includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data of a person, as described in further detailbelow.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1, physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a server may present to user aset of assessment questions designed or intended to evaluate a currentstate of mind of the user, a current psychological state of the user, apersonality trait of the user, or the like; at least a server mayprovide user-entered responses to such questions directly as at least aphysiological data and/or may perform one or more calculations or otheralgorithms to derive a score or other result of an assessment asspecified by one or more testing protocols, such as automatedcalculation of a Stanford-Binet and/or Wechsler scale for IQ testing, apersonality test scoring such as a Myers-Briggs test protocol, or otherassessments that may occur to persons skilled in the art upon reviewingthe entirety of this disclosure.

With continued reference to FIG. 1, assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device 116; third-party device 116 may include, withoutlimitation, a server or other device (not shown) that performs automatedcognitive, psychological, behavioral, personality, or other assessments.Third-party device 116 may include a device operated by an informedadvisor. An informed advisor may include any medical professional whomay assist and/or participate in the medical treatment of a user. Aninformed advisor may include a medical doctor, nurse, physicianassistant, pharmacist, yoga instructor, nutritionist, spiritual healer,meditation teacher, fitness coach, health coach, life coach, and thelike.

With continued reference to FIG. 1, physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1, physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or autoimmune disorders.

With continued reference to FIG. 1, user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1, epigenetic, as used herein, includesany user body measurements describing changes to a genome that do notinvolve corresponding changes in nucleotide sequence. Epigenetic bodymeasurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1, gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1, gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1, gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,campylobacter species, clostridium difficile, cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1, microbiome, as used herein, includesecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease-causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen-based breath tests, fructose-basedbreath tests, helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1, nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1, nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1, genetic as used herein, includes anyinherited trait. Inherited traits may include genetic material containedwith DNA including for example, nucleotides. Nucleotides include adenine(A), cytosine (C), guanine (G), and thymine (T). Genetic information maybe contained within the specific sequence of an individual's nucleotidesand sequence throughout a gene or DNA chain. Genetics may include how aparticular genetic sequence may contribute to a tendency to develop acertain disease such as cancer or Alzheimer's disease.

With continued reference to FIG. 1, genetic body measurement may includeone or more results from one or more blood tests, hair tests, skintests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1, genetic body measurement may includean analysis of COMT gene that is responsible for producing enzymes thatmetabolize neurotransmitters. Genetic body measurement may include ananalysis of DRD2 gene that produces dopamine receptors in the brain.Genetic body measurement may include an analysis of ADRA2B gene thatproduces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vasodilation and vasoconstriction of blood vessels.

With continued reference to FIG. 1, genetic body measurement may includeACE gene that is involved in producing enzymes that regulate bloodpressure. Genetic body measurement may include SLCO1B1 gene that directspharmaceutical compounds such as statins into cells. Genetic bodymeasurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fulness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1, genetic body measurement may includeCYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1, metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1, physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MM) equipment, ultrasound equipment,optical scanning equipment such as photo-plethysmographic equipment, orthe like. A sensor may include any electromagnetic sensor, includingwithout limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100. User data may includea profile, such as a psychological profile, generated using previousitem selections by the user; profile may include, without limitation, aset of actions and/or navigational actions performed as described infurther detail below, which may be combined with biological extraction108 data and/or other user data for processes such as classification touser sets as described in further detail below.

Still referring to FIG. 1, retrieval of biological extraction 108 mayinclude, without limitation, reception of biological extraction 108 fromanother computing device 104 such as a device operated by a medicaland/or diagnostic professional and/or entity, a user client device,and/or any device suitable for use as a third-party device as describedin further detail below. Biological extraction 108 may be received via aquestionnaire posted and/or displayed on a third-party device asdescribed below, inputs to which may be processed as described infurther detail below. Alternatively or additionally, biologicalextraction 108 may be stored in and/or retrieved from a user database112. User database 112 may include any data structure for orderedstorage and retrieval of data, which may be implemented as a hardware orsoftware module. A user database 112 may be implemented, withoutlimitation, as a relational database, a key-value retrieval datastoresuch as a NOSQL database, or any other format or structure for use as adatastore that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. A user database 112 mayinclude a plurality of data entries and/or records corresponding to usertests as described above. Data entries in a user database 112 may beflagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina user database 112 may reflect categories, cohorts, and/or populationsof data consistently with this disclosure. User database 112 may belocated in memory of computing device 104 and/or on another device inand/or in communication with system 100.

Referring now to FIG. 2, an exemplary embodiment of a user database 112is illustrated. One or more tables in user database 112 may include,without limitation, a user history table 204, which may be used to storedata describing past user requests, educational activities, employmenthistory, or the like. One or more tables in user database 112 mayinclude, without limitation, user preference table 208, which may beused to store one or more user preferences with regard to cost,geographic location, or the like. One or more tables in user database112 may include, without limitation, a biological extraction table 212,which may be used to store biological extraction 108 data. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various alternative or additional data which may be storedin user database 112, including without limitation any data concerningany user activity, demographics, profile information, viewing and/ormedia consumption history, or the like.

Referring again to FIG. 1, and as noted above, retrieval of biologicalextract may be performed multiple sequential and/or concurrent times,and any process using biological extract as described below may beperformed multiple sequential and/or concurrent times; likewise,biological extract may include multiple elements of physiological data,which may be used in combination for any determination and/or otherprocesses as described below.

Still referring to FIG. 1, computing device 104 is configured to receivean educational inquiry from a third-party device 116. An “educationalinquiry,” as used herein, is a datum requesting a recommended choice ofeducational resources. An educational inquiry may include, withoutlimitation, an inquiry seeking to determine what school or schools maybe best for a user to attend, where a school may include grade school,middle school, high school, college, secondary education and the like.An educational inquiry may seek a recommendation of adult educationclasses a user should enroll in. An educational inquiry may seek adetermination of what teaching style is best for a user to learninformation. An educational inquiry may seek a determination of whatlearning style is best for a user to understand information. Aneducational inquiry may seek to determine an appropriate class size fora user. An educational inquiry may seek to determine which subject ofstudy, degree, major, concentration, or the like is appropriate for auser. An educational inquiry may include an inquiry regarding a suitableeducational institution. An educational inquiry may include an inquiryregarding a suitable form of instruction, such as large class, lecturehall, live-streamed, and/or online instruction. An educational inquirymay include an inquiry regarding a learning style of the user. Aneducational inquiry may include an inquiry regarding a program of study,such as a 4-year program versus 2-year program, a graduate program, aprogram offering internships, coops, or the like. An educational inquirymay include an inquiry regarding mental health supports, protocols,and/or procedures at an institution and/or program. An educationalinquiry may include an inquiry regarding special needs supports,protocols, and/or procedures at an institution and/or program. Aneducational inquiry may include an inquiry regarding disabilitysupports, protocols, and/or procedures, such as accommodations forlearning, mental, and/or physical disabilities, at an institution and/orprogram. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples of educationalinquiries that are within the scope of this disclosure. Each inquiryand/or type of inquiry may correspond to one or more machine-learningprocesses 120, as described below, which may be used to generateresponses as described below.

With continued reference to FIG. 1, third-party device 116 may includeany device suitable for use as computing device 104, as described above.Third-party device 116 may include, without limitation, a deviceoperated by user, a device operated by an educational institution, adevice operated by an entity and/or person offering educational adviceand/or assistance in answering educational inquiries, such as a purveyorof a website offering such a service; alternatively or additionally,computing device 104 may offer such a service via a graphical userinterface such as a web page or native application on a user-operatedcomputing device 104, which may be third-party device 116. Educationalinquiry may be generated by third-party device 116 automatically,assembled using user selections of menu items, for instance fromdrop-down menus, user selections of checkboxes, radio buttons, or otherpre-populated and/or pre-arranged data elements that may be selected viauser actions.

Alternatively or additionally, and further referring to FIG. 1,educational request may include a textual word and/or phrase, such as aneducational request that a person might enter on a graphical userinterface as described above. Computing device 104 may parse a textualword or phrase to generate one or more keywords, where keywords mayinclude single words and/or phrases of two or more words. Computingdevice 104 may, for instance, tokenize a textual word or phrase toseparate the textual word or phrase into individual words. Computingdevice 104 may filter out “stop words” that do not convey meaning, suchas “of,” “a,” “an,” “the,” or the like. Computing device 104 may usewords parsed from educational request directly as keywords for retrievalfrom a database, index, or other datastore. Alternatively oradditionally, Computing device 104 may generate phrases to use askeywords and/or map one or more words or phrases from a textual word orphrase to a keyword query for retrieval from a database, index, or otherdatastore using a language processing module. Language processing modulemay include any hardware and/or software module. Language processingmodule may be configured to extract, from the one or more documents, oneor more words. One or more words may include, without limitation,strings of one or characters, including without limitation any sequenceor sequences of letters, numbers, punctuation, diacritic marks, chemicalsymbols and formulas, spaces, whitespace, and other symbols, includingany symbols usable as textual data as described above. Textual data maybe parsed into tokens, which may include a simple word (sequence ofletters separated by whitespace) or more generally a sequence ofcharacters as described previously. The term “token,” as used herein,refers to any smaller, individual groupings of text from a larger sourceof text; tokens may be broken up by word, pair of words, sentence, orother delimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 1, a language processing module may operate toproduce a language processing model. Language processing model mayinclude a program automatically generated by computing device 104 and/orlanguage processing module to produce associations between one or morewords extracted from at least a document and detect associations,including without limitation mathematical associations, between suchwords. Associations between language elements, where language elementsinclude for purposes herein extracted words, may include, withoutlimitation, mathematical associations, including without limitationstatistical correlations between any language element and any otherlanguage element and/or language elements. Statistical correlationsand/or mathematical associations may include probabilistic formulas orrelationships.

With continued reference to FIG. 1, language processing module and/orcomputing device 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between words. There may be a finite numberof other words and/or phrases to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module maycombine two or more approaches. For instance, and without limitation,machine-learning program may use a combination of Naive-Bayes (NB),Stochastic Gradient Descent (SGD), and parameter grid-searchingclassification techniques; the result may include a classificationalgorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module may use a corpusof documents to generate associations between language elements in alanguage processing module, and language processing module and/orcomputing device 104 may then use such associations to analyze wordsextracted from one or more documents and determine relationships betweensuch words. Corpus of documents may include any set of documents, suchas a plurality of web pages, textual conversation logs, articles, blogposts, excerpts from and/or electronic texts of books or the like.Alternatively or additionally, language processing module may generate amodel using correlations between words and/or phrases compiled by athird party, such as the n-grams database provided by Alphabet, Inc. ofMountain View, Calif.

With continued reference to FIG. 1, a textual word or phrase entry maybe performed by offering an entry field to a user in a graphical userinterface (GUI), which may be provided to a display of a user device.GUI may offer user options such as date ranges, content categories suchas videos, images, general content, news, shopping, maps, and the like.GUI may provide one or more options for forms of search; for instance,GUI may provide natural language searching, which may utilize a languageprocessing module as described above to process sentences or phrasesentered by a user in a manner similar to a conversational question. GUImay provide an option to enter a Boolean search; in other words,computing device 104 may parse a textual word or phrase providingeducational request for Boolean operators such as AND, OR, NOT and thelike, and apply logic of such operators to search results using programcommands implementing Boolean logic. GUI may be used to offer proximitysearch, where a distance between keywords from query in content atresult URLs is used as a ranking or selection criterion thereof;alternatively or additionally, searching for an exact phrase and/orsynonymous or closely related phrases may be performed using, withoutlimitation, language processing modules and/or language processingmodels such as vector spaces, effectively adding proximity betweenkeywords and/or synonyms thereof to criteria for finding and/or rankingsearch results.

Still referring to FIG. 1, computing device 104 is configured to select,at least a machine-learning process 120, based on the educationalinquiry. A “machine learning process” is a process that automatedly usesa body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computing device104/module to produce outputs given data provided as inputs; this is incontrast to a non-machine learning software program where the commandsto be executed are determined in advance by a user and written in aprogramming language. At least a machine-learning process 120 may beused by computing device 104 to generate an inquiry response asdescribed in further detail below.

Still referring to FIG. 1, computing device 104 may store at least amachine-learning process 120 in and/or select at least amachine-learning process 120 from a process database 124. Processdatabase 124 may include any database suitable for use as a userdatabase 112 as described above; process database 124 may, as anon-limiting example, relate each machine-learning process 120 to anentry in one or more indices, such as indices of machine-learningprocess 120 identifiers, indices and/or links to tables of user data,biological extraction 108 data, and/or identifiers of one or moreeducational inquiries. For instance, and without limitation, one or moreuser selections of graphical user interface elements as described above,such as without limitation drop-down menu items or the like, may becombined to query process database 124, returning at least amachine-learning process 120. One or more user textual entries may bemapped by a language processing module, model, and/or process to dataused as index entries, which may then be used to form a query, which maycombine such entries with user selections, to retrieve at least amachine-learning process 120.

Alternatively or additionally, and still referring to FIG. 1, computingdevice 104 may select at least a machine-learning process 120 usingmachine learning. For instance, and without limitation, computing device104 may be configured to select the at least a machine-learning process120 using a process classifier 128 that inputs an educational inquiryand outputs at least a machine-learning process 120. A “classifier,” asused in this disclosure, is a machine-learning model, such as amathematical model, neural net, or program generated by a machinelearning algorithm known as a “classification algorithm,” as describedin further detail below, that sorts inputs into categories or bins ofdata, outputting the categories or bins of data and/or labels associatedtherewith. Process classifier 128 may include a classifier configured toinput user data and output user set identifiers; process classifier 128may include a classifier configured to input biological extractions 108and output user set identifiers.

Computing device 104 and/or another device may generate processclassifier 128 using a classification algorithm, defined as a processeswhereby a computing device 104 derives a classifier from processclassification training data 132. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

With continued reference to FIG. 1, process classification training data132 may be any training data. “Training data,” as used in thisdisclosure, is data containing correlations that a machine-learningprocess 120 may use to model relationships between two or morecategories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses 120 as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by computing device 104 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

Still referring to FIG. 1, training data used to generate processclassifier 128 may include, without limitation, a plurality ofeducational inquiries, each educational inquiry including one or moreeducational inquiries, and one or more correlated identifiers ofmachine-learning processes 120. Such correlations may be identified,without limitation, by user entries and/or expert entries identifyingassociations between particular selections and/or text entries andparticular machine-learning processes 120 and/or by user feedback; forinstance, an earlier iteration of a method as disclosed herein maygenerate an outcome and receive user feedback indicating a degree ofaccuracy of the outcome and/or appropriateness of a category of questionused to produce the outcome. Process classification training data 132may additionally include entries correlating biological extractions 108to identifiers of machine-learning processes 120, which correlations maybe identified as described above.

Still referring to FIG. 1, computing device 104 may be configured togenerate process classifier 128 using a Naive Bayes classificationalgorithm. Naive Bayes classification algorithm generates classifiers byassigning class labels to problem instances, represented as vectors ofelement values. Class labels are drawn from a finite set. Naive Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naive Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1, computing device 104 may beconfigured to generate process classifier 128 using a K-nearestneighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used inthis disclosure, includes a classification method that utilizes featuresimilarity to analyze how closely out-of-sample- features resembletraining data to classify input data to one or more clusters and/orcategories of features as represented in training data; this may beperformed by representing both training data and input data in vectorforms, and using one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n) a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values. As a non-limiting example, K-nearest neighborsalgorithm may be configured to classify an input vector including aplurality of user-entered words and/or phrases, user selections, and/orelements of physiological data to clusters representing machine-learningprocesses 120.

Still referring to FIG. 1, computing device 104 may additionally beconfigured to select at least a machine-learning process 120 as afunction of biological extraction 108. For instance, a classifier may begenerated, for instance using classification algorithms and/or trainingdata as described above, that inputs biological extraction 108 andoutputs at least a machine-learning process 120. This may be based,without limitation, on one or more identified mental, emotional,psychological, and/or physiological needs of user as identified byand/or in biological extraction 108 such needs may include, withoutlimitation, diagnoses, disabilities, or other conditions affectinglearning ability, such as learning disabilities, autism spectrumdisorders, dyslexia,. Identification of needs may be performed, withoutlimitation by generation of one or more prognostic labels, for instanceas described in U.S. Nonprovisional application Ser. No. 16/372,512,filed on Apr. 2, 2019, and titled “METHODS AND SYSTEMS FOR UTILIZINGDIAGNOSTICS FOR INFORMED VIBRANT CONSTITUTIONAL GUIDANCE,” the entiretyof which is incorporated herein by reference. Identification of needsmay be performed using a user effective age, which may be calculated asdescribed in U.S. Nonprovisional application Ser. No. 16/558,502, filedon Sep. 3, 2019, and entitled “SYSTEMS AND METHODS FOR SELECTING ANINTERVENTION BASED ON EFFECTIVE AGE,” the entirety of which isincorporated herein by reference. In an embodiment, computing device 104may generate a first set of one or more machine-learning processes 120as a function of educational inquiry, generate a second set of one ormore machine-learning processes 120 as a function of biologicalextraction 108, and combine the first set and the second set as the atleast a machine-learning process 120; combination may include detectingthat two of the selected machine-learning processes 120 are duplicatesand eliminating one of the two selected machine-learning processes 120.Alternatively or additionally, process classifier 128 may takecombinations of physiological data and educational inquiry data asinputs and output machine-learning processes 120. Selection of at leasta machine-learning process 120 may alternatively or additionally beperformed using any machine-learning process 120 described in thisdisclosure. Alternatively or additionally, user may one or more criteriaaccording to which user wishes to evaluate and/or rank options, whichcomputing device 104 may use to select at least a machine-learningprocess 120, for instance by retrieval from process database 124 and/orusing process classifier 128.

With continued reference to FIG. 1, computing device 104 is configuredto generate, using the at least a machine-learning process 120 and thebiological extraction 108, an inquiry response 136. An “inquiryresponse,” as used in this disclosure, is an element of data containinga recommended educational decision, such as a recommended educationaldecision that answers a question and/or request for guidance in aneducational inquiry. As a non-limiting example, an educational inquirythat contains a question regarding what a user should major in atcollege may cause computing device 104 to generate, using at least amachine-learning process 120 a response that contains a ranked list ofmajors the user should consider majoring in, an educational inquiry thatcontains a question regarding what school a user should attend may causecomputing device 104 to generate, using at least a machine-learningprocess 120 a response that lists a recommended school and/or a. rankedlist of schools, As a further non-limiting example, an educationalinquiry that contains a question regarding what style of instruction auser should receive may cause computing device 104 to generate, using atleast a machine-learning process 120, a response that recommends aninstructional style and/or a ranked list of instructional styles.

Still referring to FIG. 1, each machine-learning process 120 of at leasta machine-learning process 120 may be trained using biologicalextraction training data 140, which may include any training data, asdescribed above, correlating physiological data, prognostic labels,labels identifying treatments, therapies or the like tending toalleviate and/or aid with conditions identified by prognostic labels,and/or other data to each other. Biological extraction training data 140may be received in any suitable form, including as user entries rankingoptions according to any or all criteria described above; such userentries may be combined and/or correlated with user biologicalextraction 108 data. Biological extraction training data 140 may includedescriptions of services, facilities, class formats, class sizes, modeof delivery of instruction, mental health support facilities and/orprograms, special needs support and/or programs, learning disabilitysupport and/or programs, or the like, which may be associated and/orcorrelated with user entries and/or biological extractions 108 asdescribed above. Biological extraction training data 140 may includeexpert entries describing programs and/or facilities needed and/ordesirable for given elements and/or sets of physiological data; expertentries may be generated and/or received according to any process forreception of expert entries described in this disclosure and/or in anydisclosure incorporated herein by reference. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousalternative or additional entries and/or data usable as biologicalextraction training data 140 may be provided.

Still referring to FIG. 1, selection of at least a machine-learningprocess 120 may include selection of a machine-learning model, atraining data set to be used in a machine-learning algorithm and/or toproduce a machine-learning model, and/or a machine-learning algorithmsuch as lazy-learning and/or model production, or the like. Computingdevice 104 may be designed and configured to create a machine-learningmodel using techniques for development of linear regression models.Linear regression models may include ordinary least squares regression,which aims to minimize the square of the difference between predictedoutcomes and actual outcomes according to an appropriate norm formeasuring such a difference (e.g. a vector-space distance norm);coefficients of the resulting linear equation may be modified to improveminimization. Linear regression models may include ridge regressionmethods, where the function to be minimized includes the least-squaresfunction plus term multiplying the square of each coefficient by ascalar amount to penalize large coefficients. Linear regression modelsmay include least absolute shrinkage and selection operator (LASSO)models, in which ridge regression is combined with multiplying theleast-squares term by a factor of 1 divided by double the number ofsamples. Linear regression models may include a multi-task lasso modelwherein the norm applied in the least-squares term of the lasso model isthe Frobenius norm amounting to the square root of the sum of squares ofall terms. Linear regression models may include the elastic net model, amulti-task elastic net model, a least angle regression model, a LARSlasso model, an orthogonal matching pursuit model, a Bayesian regressionmodel, a logistic regression model, a stochastic gradient descent model,a perceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 1, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 1, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training dataset are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data.

Continuing to refer to FIG. 1, machine-learning algorithms may includesupervised machine-learning algorithms. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude elements of physiological data as described above as inputs,inquiry responses 136 as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various possible variations ofsupervised machine learning algorithms that may be used to determinerelation between inputs and outputs. Supervised machine-learningprocesses 120 may include classification algorithms as defined above.

Still referring to FIG. 1, machine learning processes may includeunsupervised processes. An unsupervised machine-learning process 120, asused herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning process120 may be free to discover any structure, relationship, and/orcorrelation provided in the data. Unsupervised processes may not requirea response variable; unsupervised processes may be used to findinteresting patterns and/or inferences between variables, to determine adegree of correlation between two or more variables, or the like.

With continued reference to FIG. 1, machine-learning processes 120 asdescribed in this disclosure may be used to generate machine-learningmodels. A machine-learning model, as used herein, is a mathematicalrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process 120 including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processes120 to calculate an output datum. As a further non-limiting example, amachine-learning model may be generated by creating an artificial neuralnetwork, such as a convolutional neural network comprising an inputlayer of nodes, one or more intermediate layers, and an output layer ofnodes. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 1, at least a machine-learning process 120 mayinclude a lazy-learning process and/or protocol, which may alternativelybe referred to as a “lazy loading” or “call-when-needed” process and/orprotocol, may be a process whereby machine learning is conducted uponreceipt of an input to be converted to an output, by combining the inputand training set to derive the algorithm to be used to produce theoutput on demand. For instance, an initial set of simulations may beperformed to cover an initial heuristic and/or “first guess” at anoutput and/or relationship. As a non-limiting example, an initialheuristic may include a ranking of associations between inputs andelements of training data. Heuristic may include selecting some numberof highest-ranking associations and/or training data elements. Lazylearning may implement any suitable lazy learning algorithm, includingwithout limitation a K-nearest neighbors algorithm, a lazy naive Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

With continued reference to FIG. 1, at least a machine learning processmay include a process classifying and/or scoring options according toany criterion used for and/or referred to in any educational inquiry.For instance, and without limitation, at least a machine-learningprocess 120 may include a mental health suitability classificationprocess and/or scoring algorithm that scores and/or classifies optionsaccording to quality and/or availability of mental health supportsand/or protocols, as needed by or necessary given biological extraction108, and/or according to degree of mental health issues in a studentbody as relevant to biological extraction 108. As a further example, atleast a machine-learning process 120 may include a special needssuitability classification process and/or scoring algorithm that scoresand/or classifies options according to quality and/or availability ofspecial needs supports and/or protocols, as needed by or necessary givenbiological extraction 108. As a further example, at least amachine-learning process 120 may include a disability accommodationsuitability classification process and/or scoring algorithm that scoresand/or classifies options according to quality and/or availability ofdisability accommodations such as without limitation learning disabilityaccommodations, as needed by or necessary given biological extraction108.

Continuing to refer to FIG. 1, where at least a machine-learning process120 is a single process, inquiry response 136 may be presented to user,for instance by presentation of a recommended institution, major, class,instruction style, or learning environment, and/or a ranked listthereof. Where at least a machine-learning process 120 includes aplurality of machine-learning processes 120, computing device 104 maycombine outputs thereof to generate educational inquiry. For instance,computing device 104 and/or process classifier 128 as described abovemay select a first machine-learning process 120 to recommend one or moreeducational institutions based on suitability of instruction format fora user, a second machine-learning process 120 to recommend one or moreeducational institutions based on a degree of mental health support, athird machine-learning process 120 to recommend one or more educationalinstitutions based on a degree of support for a given learningdisability and/or special need, and/or one or more additionalmachine-learning processes 120. Each process may produce arecommendation, for instance using a classifier as described above,and/or may produce a score, such as an output of a regression modeland/or a degree of proximity in a classifier such as a KNN model and/orlazy-learning process as described above. Scores output by eachmachine-learning process 120 of at least a machine-learning process 120may be used in combination to generate a recommendation and/or a rankedlist of options. Use of outputs in combination may include calculationof an aggregate score, for instance by adding, multiplying, or averagingscores; options may be ranked by aggregate score and/or an option havinga maximal aggregate score may be selected. Individual outputs mayalternatively or additionally be presented to a user by category.

Alternatively or additionally, and still referring to FIG. 1, each scorecorresponding to an option may be used as an attribute of a vector asdescribed above, and proximity of each such vector to a user vector ofideal or optimal values for a user may be calculated using any measureof vector distance as described above; options may be ranked accordingto degree of proximity to the user vector. User vector may be determinedby recording one or more user preferences and quantifying such userpreferences as elements of user vector; a user preference may berecorded and quantified per attribute of vectors associated withoptions. For instance, a graphical user interface may display at userdevice options to rate one or more priorities absolutely and/orrelatively to each other, for instance by providing a numerical ratingscale with radio buttons and/or drop-down lists, sliders where a usermay set relative importance along a continuum for each user vectorattribute, and/or textual entry fields wherein a user may enter numbersreflecting user's personal degree of importance for each field.

With continued reference to FIG. 1, each option of the plurality ofoptions includes an option vector including a plurality of option vectorentries; option vector, in any given embodiment, may have entriescorresponding to entries in user vector. For instance, and withoutlimitation, each option element may have an associated vector, which maybe chosen as a subset of attributes of option element listed in adatabase, where each attribute indicates an effect each option elementhas on an attribute in user vector. In an embodiment, selecting and/orranking one or more options may include generating a loss function ofthe plurality of options and the user vector, minimizing the lossfunction, and selecting an option from the plurality of options as afunction of minimizing the loss function. A “loss function” as usedherein is an expression of an output of which an optimization algorithmminimizes to generate an optimal result. As a non-limiting example,computing device 104 may select an option having an associated vectorthat minimizes a measure of difference from user vector; measure ofdifference may include, without limitation, a measure of geometricdivergence between option vector and user vector, such as withoutlimitation cosine similarity or may include any suitable error functionmeasuring any degree of divergence and/or aggregation of degrees ofdivergence, between attributes of user heath quality vector and optionvectors. Selection of different loss functions may result inidentification of different options as generating minimal outputs.Alternatively or additionally, each of user vector and each optionvector may be represented by a mathematical expression having the sameform as mathematical expression; computing device 104 may compare theformer to the latter using an error function representing averagedifference between the two mathematical expressions. Error function may,as a non-limiting example, be calculated using the average differencebetween coefficients corresponding to each variable. An option having amathematical expression minimizing the error function may be selected,as representing an optimal expression of relative importance ofvariables to a system or user. In an embodiment, error function and lossfunction calculations may be combined; for instance, a variableresulting in a minimal aggregate expression of error function and lossfunction, such as a simple addition, arithmetic mean, or the like of theerror function with the loss function, may be selected, corresponding toan option that minimizes total variance from optimal variables whilesimultaneously minimizing a degree of variance from a set of prioritiescorresponding to variables. Coefficients of mathematical expressionand/or loss function may be scaled and/or normalized; this may permitcomparison and/or error function calculation to be performed withoutskewing by varied absolute quantities of numbers. Server may select aplurality of options to user; for instance, ranking may be maintained ofoptions according to a degree to which they minimize loss function, anda number of highest-ranking options, such as the ten highest rankingoptions or the like, may be selected. Alternatively or additionally,relative ranking, one or more scores produced by machine-learningprocesses 120, and/or an aggregate score may be displayed to user alongwith an option, for instance if the user has selected that option. As anon-limiting example, if a user has selected and/or searched for aparticular institution, major, program, or the like, one or more scoresand/or rankings associated with the particular institution, major,program, or the like may be displayed to the user.

In an embodiment, and with continued reference to FIG. 1, user vectorand vectors associated with options may include one or more attributesgenerated using at least a machine-learning process 120 and one or moreadditional attributes; one or more additional attributes may beretrieved and/or received in any suitable manner, including withoutlimitation storage in a database listing such attributes. One or moreadditional attributes may be provided, for instance, by educationalinstitutions, ranking bodies, third-party information sources, or thelike. Such additional attributes may include, without limitation, costof tuition, housing, materials, or the like, geographic location,academic reputation and/or ranking, degree of difficulty of study interms of hours per week required or the like, acceptance and/orgraduation percentages, programs offered, services offered, and/or anyother criterion used to select educational institutions, and/or anyother quantifiable attributes usable for and/or suitable for selectionof options such as educational institutions, courses of study, or thelike as described above. As a non-limiting example, user and optionvectors may contain a single output of a single machine-learning process120, for instance selected based on a user entry, and one or moreadditional attributes; determination of proximity of option vectors touser vector and selection, scoring, and/or ranking thereby may beperformed as described above.

Referring now to FIG. 3, an exemplary embodiment of an artificialintelligence method 300 of generating educational inquiry responses 136from biological extractions 108. At step 305, computing device 104retrieves a biological extraction 108 pertaining to a user; this may beimplemented as described above in reference to FIGS. 1-2. Biologicalextraction 108 may include a psychological profile; psychologicalprofile may be obtained utilizing a questionnaire performed by the user.Biological extraction 108 may include a genetic sequence.

At step 310, and still referring to FIG. 3, computing device 104receives an educational inquiry from a third-party device 116; this maybe implemented as described above in reference to FIGS. 1-2. Educationalinquiry may include an inquiry regarding a suitable educationalinstitution. Educational inquiry may include an inquiry regarding asuitable form of instruction. Educational inquiry may include an inquiryregarding a learning style of the user.

At step 315, and continuing to refer to FIG. 3, the computing device 104selects at least a machine-learning process 120 based on the educationalinquiry; this may be implemented as described above in reference toFIGS. 1-2. Selecting at least a machine-learning process 120 may includeselecting the at least a machine-learning process 120 using a classifierthat inputs educational inquiry and outputs at least a machine-learningprocess 120. Selecting the at least a machine-learning process 120 mayinclude selecting the at least a machine-learning process 120 as afunction of biological extraction 108. At least a machine-learningprocess 120 may include a mental health suitability classificationprocess.

At step 320, generating, by the computing device 104 and using the atleast a machine-learning process 120 and the biological extraction 108,an inquiry response 136; this may be implemented as described above inreference to FIGS. 1-2.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 4 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 400 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 400 includes a processor 404 and a memory408 that communicate with each other, and with other components, via abus 412. Bus 412 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 404 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 404 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 404 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 408 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 416 (BIOS), including basic routines that help totransfer information between elements within computer system 400, suchas during start-up, may be stored in memory 408. Memory 408 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 420 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 408 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 400 may also include a storage device 424. Examples of astorage device (e.g., storage device 424) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 424 may be connected to bus 412 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 424 (or one or morecomponents thereof) may be removably interfaced with computer system 400(e.g., via an external port connector (not shown)). Particularly,storage device 424 and an associated machine-readable medium 428 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 400. In one example, software 420 may reside, completelyor partially, within machine-readable medium 428. In another example,software 420 may reside, completely or partially, within processor 404.

Computer system 400 may also include an input device 432. In oneexample, a user of computer system 400 may enter commands and/or otherinformation into computer system 400 via input device 432. Examples ofan input device 432 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 432may be interfaced to bus 412 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 412, and any combinations thereof. Input device 432 mayinclude a touch screen interface that may be a part of or separate fromdisplay 436, discussed further below. Input device 432 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 400 via storage device 424 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 440. A network interfacedevice, such as network interface device 440, may be utilized forconnecting computer system 400 to one or more of a variety of networks,such as network 444, and one or more remote devices 448 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 444,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 420,etc.) may be communicated to and/or from computer system 400 via networkinterface device 440.

Computer system 400 may further include a video display adapter 452 forcommunicating a displayable image to a display device, such as displaydevice 436. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 452 and display device 436 may be utilized incombination with processor 404 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 400 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 412 via a peripheral interface 456. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An artificial intelligence system for generating educational inquiryresponses from biological extractions, the system comprising a computingdevice, the computing device designed and configured to: retrieve abiological extraction pertaining to a user; receive, from a third-partydevice, an educational inquiry; select, based on the educationalinquiry, at least a machine-learning process; and generate, using the atleast a machine-learning process and the biological extraction, aninquiry response.
 2. The system of claim 1, wherein the biologicalextraction includes a psychological profile.
 3. The system of claim 2,wherein the psychological profile is obtained utilizing a questionnaireperformed by the user.
 4. The system of claim 1, wherein the biologicalextraction includes a genetic sequence.
 5. The system of claim 1,wherein the educational inquiry is an inquiry regarding a suitableeducational institution.
 6. The system of claim 1, wherein theeducational inquiry is an inquiry regarding a suitable form ofinstruction.
 7. The system of claim 1, wherein the educational inquiryis an inquiry regarding a learning style of the user.
 8. The system ofclaim 1, wherein the computing device is further configured to selectthe at least a machine-learning process using a classifier that inputsthe educational inquiry and outputs at least a machine-learning process.9. The system of claim 1, wherein the computing device is furtherconfigured to select the at least a machine-learning process as afunction of the biological extraction.
 10. The system of claim 1,wherein the at least a machine-learning process further includes amental health suitability classification process.
 11. An artificialintelligence method of generating educational inquiry responses frombiological extractions, the method comprising: retrieving, by acomputing device, a biological extraction pertaining to a user;receiving, by the computing device and from a third-party device, aneducational inquiry; selecting, by the computing device and based on theeducational inquiry, at least a machine-learning process; andgenerating, by the computing device and using the at least amachine-learning process and the biological extraction, an inquiryresponse.
 12. The method of claim 11, wherein the biological extractionincludes a psychological profile.
 13. The method of claim 12, whereinthe psychological profile is obtained utilizing a questionnaireperformed by the user.
 14. The method of claim 11, wherein thebiological extraction includes a genetic sequence.
 15. The method ofclaim 11, wherein the educational inquiry is an inquiry regarding asuitable educational institution.
 16. The method of claim 11, whereinthe educational inquiry is an inquiry regarding a suitable form ofinstruction.
 17. The method of claim 11, wherein the educational inquiryis an inquiry regarding a learning style of the user.
 18. The method ofclaim 11, wherein selecting the at least a machine-learning processfurther comprises selecting the at least a machine-learning processusing a classifier that inputs the educational inquiry and outputs atleast a machine-learning process.
 19. The method of claim 11, whereinselecting the at least a machine-learning process further comprisesselecting the at least a machine-learning process as a function of thebiological extraction.
 20. The method of claim 11, wherein the at leasta machine-learning process further includes a mental health suitabilityclassification process.